Decision-Theoretic Plan Failure Debugging and Repair

A number of strategies exist for the recovery from execution-time plan failures. One manner in which these strategies difFer is the degree of dependence on the reliability and availability of the planner’s knowledge. The best strategy, however, may be dependent on a number of considerations, including the type of plan failure, the criticality of the failure, the availability of resources, and the reliability and availability of the knowledge involved in a given plan failure instance. We are examining a decision-theoretic approach to diagnose plan fakes and to dynamically select fkom multiple failure recovery strategies when an execution-time plan failure occurs. Existing failure recovery strategies generally classify, with assumed or proven certainty, the type of error that occurred during plan execution and then select a fixed strategy to recover from that error. Assumptions regarding the accuracy and completeness of the planner’s domain model vary. On one end are approaches that use deterministic heuristics or purely syntactic analyses to debug and repair. These approaches are efficient and require limited knowledge, but generally are limited in the level of diagnosis and repair they can perform. On the other end are logic-based approaches, that are robust, but are knowledge and resource intensive. Such approaches are not always feasible. Incomplete or uncertain knowledge of previous planning actions, as in multi-agent planners, prechrdes a complete logical analysis. Even when possible, the costs of collecting and reasoning with complete information may be intractable, especially given time pressures and other resource constraints. The goal of our work is the development of an approach that can intelligently select and apply ftilure recovery strategies that are appropriate to the situation and that can cope with uncertainty. There are three primary components of our research: diagnosis of plan failures, plan repair and planner modification. When a failure is detected, we use a